Characterizing Trastuzumab-Induced Alterations in Intratumoral Heterogeneity with Quantitative Imaging and Immunohistochemistry in HER2+ Breast Cancer

Systematic evaluation of MRI-based characterization of tumor-associated vascular morphology and hemodynamics via a dynamic digital phantom

Purpose

Validation of quantitative imaging biomarkers is a challenging task, due to the difficulty in measuring the ground truth of the target biological process. A digital phantom-based framework is established to systematically validate the quantitative characterization of tumor-associated vascular morphology and hemodynamics based on dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI).

Approach

A digital phantom is employed to provide a ground-truth vascular system within which 45 synthetic tumors are simulated. Morphological analysis is performed on high-spatial resolution DCE-MRI data (spatial/temporal resolution = 30 to 300    μ m / 60    s ) to determine the accuracy of locating the arterial inputs of tumor-associated vessels (TAVs). Hemodynamic analysis is then performed on the combination of high-spatial resolution and high-temporal resolution (spatial/temporal resolution = 60 to 300    μ m / 1 to 10 s) DCE-MRI data, determining the accuracy of estimating tumor-associated blood pressure, vascular extraction rate, interstitial pressure, and interstitial flow velocity.

Results

The observed effects of acquisition settings demonstrate that, when optimizing the DCE-MRI protocol for the morphological analysis, increasing the spatial resolution is helpful but not necessary, as the location and arterial input of TAVs can be recovered with high accuracy even with the lowest investigated spatial resolution. When optimizing the DCE-MRI protocol for hemodynamic analysis, increasing the spatial resolution of the images used for vessel segmentation is essential, and the spatial and temporal resolutions of the images used for the kinetic parameter fitting require simultaneous optimization.

Conclusion

An in silico validation framework was generated to systematically quantify the effects of image acquisition settings on the ability to accurately estimate tumor-associated characteristics.

Dual anti-HER2/EGFR inhibition synergistically increases therapeutic effects and alters tumor oxygenation in HNSCC

Epidermal growth factor receptor (EGFR), human epidermal growth factor receptor 2 (HER2), and hypoxia are associated with radioresistance. The goal of this study is to study the synergy of anti-HER2, trastuzumab, and anti-EGFR, cetuximab, and characterize the tumor microenvironment components that may lead to increased radiation sensitivity with dual anti-HER2/EGFR therapy in head and neck squamous cell carcinoma (HNSCC). Positron emission tomography (PET) imaging ([89Zr]-panitumumab and [89Zr]-pertuzumab) was used to characterize EGFR and HER2 in HNSCC cell line tumors. HNSCC cells were treated with trastuzumab, cetuximab, or combination followed by radiation to assess for viability and radiosensitivity (colony forming assay, immunofluorescence, and flow cytometry). In vivo, [18F]-FMISO-PET imaging was used to quantify changes in oxygenation during treatment. Bliss Test of Synergy was used to identify combination treatment synergy. Quantifying EGFR and HER2 receptor expression revealed a 50% increase in heterogeneity of HER2 relative to EGFR. In vitro, dual trastuzumab-cetuximab therapy shows significant decreases in DNA damage response and increased response to radiation therapy (p < 0.05). In vivo, tumors treated with dual anti-HER2/EGFR demonstrated decreased tumor hypoxia, when compared to single agent therapies. Dual trastuzumab-cetuximab demonstrates synergy and can affect tumor oxygenation in HNSCC. Combination trastuzumab-cetuximab modulates the tumor microenvironment through reductions in tumor hypoxia and induces sustained treatment synergy.

Early Identification of Pathologic Complete Response to Neoadjuvant Chemotherapy Using Multiphase DCE-MRI by Siamese Network in Breast Cancer: A Longitudinal Multicenter Study

BACKGROUND

Siamese network (SN) using longitudinal DCE-MRI for pathologic complete response (pCR) identification lack a unified approach to phases selection.

PURPOSE

To identify pCR in early-stage NAC, using SN with longitudinal DCE-MRI and introducing IPS for phases selection.

STUDY TYPE

Multicenter, longitudinal.

POPULATION

Center A: 162 female patients (50.63 ± 8.41 years) divided 7:3 into training and internal validation cohorts. Center B: 61 female patients (50.08 ± 7.82 years) were used as an external validation cohort.

FIELD STRENGTH/SEQUENCE

Center A: single vendor 3.0 T with a compressed-sensing volume interpolated breath-hold examination sequence. Center B: single vendor 1.5 T with volume interpolated breath-hold examination sequence.

ASSESSMENT

Patients underwent DCE-MRI before and after two NAC cycles, with tumor regions of interest (ROI) manually delineated. Histopathology was the reference for pCR identification. Models developed included a clinical one, four SN models based on IPS-selected phases, and integrated models combining clinical and SN features.

STATISTICAL TESTS

Model performance was evaluated using the area under the receiver operating characteristic curve (AUC). The DeLong test was used to compare AUCs. Net reclassification improvement and integrated discrimination improvement (IDI) tests were employed for performance comparison. P < 0.05 was considered significant.

RESULTS

In internal and external validation cohorts, the clinical model showed AUCs of 0.760 and 0.718. SN and integrated models, with increasing phases via IPS, achieved AUCs ranging from 0.813 to 0.951 and 0.818 to 0.922. Notably, SN-3 and integrated-3 and integrated-4 outperformed the clinical model. However, input phases beyond 20% did not significantly enhance performance (IDI test: SN-4 vs. SN-3, P = 0.314 and 0.630; integrated-4 vs. integrated-3, P = 0.785 and 0.709).

DATA CONCLUSION

The longitudinal multiphase DCE-MRI based on the SN demonstrates promise for identifying pCR in breast cancer.

EVIDENCE LEVEL

1 TECHNICAL EFFICACY: Stage 4.

Multimodal prediction of neoadjuvant treatment outcome by serial FDG PET and MRI in women with locally advanced breast cancer

PURPOSE

To investigate combined MRI and 18F-FDG PET for assessing breast tumor metabolism/perfusion mismatch and predicting pathological response and recurrence-free survival (RFS) in women treated for breast cancer.

METHODS

Patients undergoing neoadjuvant chemotherapy (NAC) for locally-advanced breast cancer were imaged at three timepoints (pre, mid, and post-NAC), prior to surgery. Imaging included diffusion-weighted and dynamic contrast-enhanced (DCE-) MRI and quantitative 18F-FDG PET. Tumor imaging measures included apparent diffusion coefficient, peak percent enhancement (PE), peak signal enhancement ratio (SER), functional tumor volume, and washout volume on MRI and standardized uptake value (SUVmax), glucose delivery (K1) and FDG metabolic rate (MRFDG) on PET, with percentage changes from baseline calculated at mid- and post-NAC. Associations of imaging measures with pathological response (residual cancer burden [RCB] 0/I vs. II/III) and RFS were evaluated.

RESULTS

Thirty-five patients with stage II/III invasive breast cancer were enrolled in the prospective study (median age: 43, range: 31-66 years, RCB 0/I: N = 11/35, 31%). Baseline imaging metrics were not significantly associated with pathologic response or RFS (p > 0.05). Greater mid-treatment decreases in peak PE, along with greater post-treatment decreases in several DCE-MRI and 18F-FDG PET measures were associated with RCB 0/I after NAC (p < 0.05). Additionally, greater mid- and post-treatment decreases in DCE-MRI (peak SER, washout volume) and 18F-FDG PET (K1) were predictive of prolonged RFS. Mid-treatment decreases in metabolism/perfusion ratios (MRFDG/peak PE, MRFDG/peak SER) were associated with improved RFS.

CONCLUSION

Mid-treatment changes in both PET and MRI measures were predictive of RCB status and RFS following NAC. Specifically, our results indicate a complementary relationship between DCE-MRI and 18F-FDG PET metrics and potential value of metabolism/perfusion mismatch as a marker of patient outcome.

Molecular and serological biomarkers to predict trastuzumab responsiveness in HER-2 positive breast cancer

HER-2-positive breast cancer is characterized by its aggressive nature, poor prognosis, and reduced overall survival. The emergence of trastuzumab resistance is currently considered a global problem. The immune system plays a pivotal role in tumor progression and development. Cytotoxic T lymphocyte-associated protein-4 (CTLA-4) and other immune checkpoint proteins may be potential prognostic factors and therapeutic targets for breast cancer. This study aimed to determine the correlation between CTLA-4 expression in peripheral blood and insulin-like growth factor-1 (IGF-1) serum levels and their impact on trastuzumab responsiveness in HER-2-positive patients with breast cancer. CTLA-4 expression was analyzed in peripheral blood cells using quantitative PCR, while IGF-1 serum levels were assessed through electrochemiluminescence assays. There was a significant increase in CTLA-4 expression at cycle 9, which continued to increase until it reached 4.6 at cycle 17. High IGF-1 levels were observed in newly diagnosed HER-2 positive patients before trastuzumab therapy, significantly decreasing post-therapy (p=0.001). Co-targeting HER-2 and IGF-1 receptors may reduce the risk of recurrence and improve outcomes. In addition, targeted CTLA-4 molecules may improve patient survival and prevent recurrence.

Molecular and functional imaging in cancer-targeted therapy: current applications and future directions

Targeted anticancer drugs block cancer cell growth by interfering with specific signaling pathways vital to carcinogenesis and tumor growth rather than harming all rapidly dividing cells as in cytotoxic chemotherapy. The Response Evaluation Criteria in Solid Tumor (RECIST) system has been used to assess tumor response to therapy via changes in the size of target lesions as measured by calipers, conventional anatomically based imaging modalities such as computed tomography (CT), and magnetic resonance imaging (MRI), and other imaging methods. However, RECIST is sometimes inaccurate in assessing the efficacy of targeted therapy drugs because of the poor correlation between tumor size and treatment-induced tumor necrosis or shrinkage. This approach might also result in delayed identification of response when the therapy does confer a reduction in tumor size. Innovative molecular imaging techniques have rapidly gained importance in the dawning era of targeted therapy as they can visualize, characterize, and quantify biological processes at the cellular, subcellular, or even molecular level rather than at the anatomical level. This review summarizes different targeted cell signaling pathways, various molecular imaging techniques, and developed probes. Moreover, the application of molecular imaging for evaluating treatment response and related clinical outcome is also systematically outlined. In the future, more attention should be paid to promoting the clinical translation of molecular imaging in evaluating the sensitivity to targeted therapy with biocompatible probes. In particular, multimodal imaging technologies incorporating advanced artificial intelligence should be developed to comprehensively and accurately assess cancer-targeted therapy, in addition to RECIST-based methods.

ImmunoPET Imaging with 89Zr-Labeled Atezolizumab Enables In Vivo Evaluation of PD-L1 in Tumorgraft Models of Renal Cell Carcinoma

PURPOSE

Immune checkpoint inhibitors (ICI) targeting the programmed cell death protein 1 and its ligand (PD-1/PD-L1) have transformed the treatment paradigm for metastatic renal cell carcinoma (RCC). However, response rates to ICIs as single agents or in combination vary widely and predictive biomarkers are lacking. Possibly related to the heterogeneity and dynamic nature of PD-L1 expression, tissue-based methods have shown limited value. Immuno-positron emission tomography (immunoPET) may enable noninvasive, comprehensive, and real-time PD-L1 detection. Herein, we systematically examined the performance of immunoPET for PD-L1 detection relative to IHC in an RCC patient-derived tumorgraft (TG) platform.

EXPERIMENTAL DESIGN

Eight independent RCC TGs with a wide range of PD-L1 expression (0%-85%) were evaluated by immunoPET. Uptake of 89Zr-labeled atezolizumab ([89Zr]Zr-DFO-ATZ) was compared with PD-L1 expression in tumors by IHC through double-blind analyses. Clinical outcomes of ICI-treated patients whose TGs were examined were analyzed to evaluate the clinical role of immunoPET in RCC.

RESULTS

ImmunoPET with [89Zr]Zr-DFO-ATZ (day 6/7 postinjection) revealed a statistically significant association with PD-L1 IHC assays (P = 0.0014; correlation ρXY = 0.78). Furthermore, immunoPET can be used to assess the heterogeneous distribution of PD-L1 expression. Finally, studies in the corresponding patients (n = 4) suggest that PD-L1 signal may influence ICI responsiveness.

CONCLUSIONS

ImmunoPET with [89Zr]Zr-DFO-ATZ may enable a thorough and dynamic assessment of PD-L1 across sites of disease. The power of immunoPET to predict ICI response in RCC is being explored in an ongoing clinical trial (NCT04006522).

A Machine Learning Model Based on Unsupervised Clustering Multihabitat to Predict the Pathological Grading of Meningiomas

Purpose

We aim to develop and validate a machine learning model by enhanced MRI to determine the pathological grading of meningiomas with unsupervised clustering image analysis method, which are multihabitat to reflect the inherent heterogeneity of tumors.

Materials and Methods

A total of 120 patients with meningiomas confirmed by postoperative pathology were included in the study, including 60 patients with low-grade meningiomas (WHO grade I) and 60 patients with high-grade meningiomas (WHO grade II and WHO grade III). All patients underwent complete head enhanced magnetic resonance scans before surgery or any anti-tumor treatment. Enrolled patients in the group received surgical resection and obtained postoperative pathological data. The patients in the training group (84 people) and the test group (36 people) were randomly divided into two groups according to the ratio of 7 to 3. Multi-habitat features were extracted from MRI images based on enhanced T1. Machine learning method was used to model, which was used to distinguish high-grade meningioma from low-grade meningioma. At the same time, the obtained machine learning model was calibrated and evaluated.

Results

In patients with low-grade meningioma and high-grade meningioma, we found significant differences in Silhouette coefficient (P<0.05). In the machine learning model, the area under the curve was 0.838 in the training group (sensitivity, 67.65%; specificity, 88.82%) and 0.73 in the test group (sensitivity, 69.05%; specificity, 71.43%). After the analysis of calibration curve and decision curve analysis, the model had shown the potential of great application value.

Conclusions

Multi-habitat analysis based on enhanced MRI (T1) could accurately predict the pathological grading of meningiomas. This unsupervised image-based method could reflect the direct heterogeneity between high-grade meningiomas and low-grade meningiomas, which is of great significance for patients' treatment and prevention of recurrence.

A novel ensemble feature selection method for pixel-level segmentation of HER2 overexpression

Classifying histopathology images on a pixel-level requires sets of features able to capture the complex characteristics of the images, like the irregular cell morphology and the color heterogeneity on the tissue aspect. In this context, feature selection becomes a crucial step in the classification process such that it reduces model complexity and computational costs, avoids overfitting, and thereby it improves the model performance. In this study, we propose a new ensemble feature selection method by combining a set of base selectors, classifiers, and rank aggregation methods, aiming to determine from any initial set of handcrafted features, a smaller set of relevant color and texture pixel-level features, subsequently used for segmenting HER2 overexpression on a pixel-level, in breast cancer tissue images. We have been able to significantly reduce the set of initial features, using the proposed ensemble feature selection method. The best results are obtained using $$\chi ^2$$ χ 2 , Random Forest, and Runoff as the based selector, classifier, and aggregation method, respectively. The classification performance of the best model trained on the selected features set results in 0.939 recall, 0.866 specificity, 0.903 accuracy, 0.875 precision, and 0.906 F1-score.

Quantifying Tumor Heterogeneity via MRI Habitats to Characterize Microenvironmental Alterations in HER2+ Breast Cancer

This study identifies physiological habitats using quantitative magnetic resonance imaging (MRI) to elucidate intertumoral differences and characterize microenvironmental response to targeted and cytotoxic therapy. BT-474 human epidermal growth factor receptor 2 (HER2+) breast tumors were imaged before and during treatment (trastuzumab, paclitaxel) with diffusion-weighted MRI and dynamic contrast-enhanced MRI to measure tumor cellularity and vascularity, respectively. Tumors were stained for anti-CD31, anti-ɑSMA, anti-CD45, anti-F4/80, anti-pimonidazole, and H&E. MRI data was clustered to identify and label each habitat in terms of vascularity and cellularity. Pre-treatment habitat composition was used stratify tumors into two "tumor imaging phenotypes" (Type 1, Type 2). Type 1 tumors showed significantly higher percent tumor volume of the high-vascularity high-cellularity (HV-HC) habitat compared to Type 2 tumors, and significantly lower volume of low-vascularity high-cellularity (LV-HC) and low-vascularity low-cellularity (LV-LC) habitats. Tumor phenotypes showed significant differences in treatment response, in both changes in tumor volume and physiological composition. Significant positive correlations were found between histological stains and tumor habitats. These findings suggest that the differential baseline imaging phenotypes can predict response to therapy. Specifically, the Type 1 phenotype indicates increased sensitivity to targeted or cytotoxic therapy compared to Type 2 tumors.

Modulation of the Tumor Microenvironment with Trastuzumab Enables Radiosensitization in HER2+ Breast Cancer

Simple Summary

Trastuzumab and radiation are used clinically to treat HER2-overexpressing breast cancers; however, the mechanistic synergy of anti-HER2 and radiation therapy has not been investigated. In this study, we identify that a subtherapeutic dose of trastuzumab sensitizes the tumor microenvironment to fractionated radiation. This results in longitudinal sustained response by triggering a state of innate immune activation through reduced DNA damage repair and increased tumor oxygenation. As positron emission tomography imaging can be used to longitudinally evaluate changes in tumor hypoxia, synergy of combination therapies is the result of both cellular and molecular changes in the tumor microenvironment.

Abstract

DNA damage repair and tumor hypoxia contribute to intratumoral cellular and molecular heterogeneity and affect radiation response. The goal of this study is to investigate anti-HER2-induced radiosensitization of the tumor microenvironment to enhance fractionated radiotherapy in models of HER2+ breast cancer. This is monitored through in vitro and in vivo studies of phosphorylated γ-H2AX, [¹⁸F]-fluoromisonidazole (FMISO)-PET, and transcriptomic analysis. In vitro, HER2+ breast cancer cell lines were treated with trastuzumab prior to radiation and DNA double-strand breaks (DSB) were quantified. In vivo, HER2+ human cell line or patient-derived xenograft models were treated with trastuzumab, fractionated radiation, or a combination and monitored longitudinally with [¹⁸F]-FMISO-PET. In vitro DSB analysis revealed that trastuzumab administered prior to fractionated radiation increased DSB. In vivo, trastuzumab prior to fractionated radiation significantly reduced hypoxia, as detected through decreased [¹⁸F]-FMISO SUV, synergistically improving long-term tumor response. Significant changes in IL-2, IFN-gamma, and THBS-4 were observed in combination-treated tumors. Trastuzumab prior to fractionated radiation synergistically increases radiotherapy in vitro and in vivo in HER2+ breast cancer which is independent of anti-HER2 response alone. Modulation of the tumor microenvironment, through increased tumor oxygenation and decreased DNA damage response, can be translated to other cancers with first-line radiation therapy.

Spatiotemporal extracellular matrix modeling for in situ cell niche studies

Extracellular matrix (ECM) components govern a range of cell functions, such as migration, proliferation, maintenance of stemness, and differentiation. Cell niches that harbor stem-/progenitor cells, with matching ECM, have been shown in a range of organs, although their presence in the heart is still under debate. Determining niches depends on a range of in vitro and in vivo models and techniques, where animal models are powerful tools for studying cell-ECM dynamics; however, they are costly and time-consuming to use. In vitro models based on recombinant ECM proteins lack the complexity of the in vivo ECM. To address these issues, we present the spatiotemporal extracellular matrix model for studies of cell-ECM dynamics, such as cell niches. This model combines gentle decellularization and sectioning of cardiac tissue, allowing retention of a complex ECM, with recellularization and subsequent image processing using image stitching, segmentation, automatic binning, and generation of cluster maps. We have thereby developed an in situ representation of the cardiac ECM that is useful for assessment of repopulation dynamics and to study the effect of local ECM composition on phenotype preservation of reseeded mesenchymal progenitor cells. This model provides a platform for studies of organ-specific cell-ECM dynamics and identification of potential cell niches.

Quantitative Image Analysis for Tissue Biomarker Use: A White Paper From the Digital Pathology Association

Tissue biomarkers have been of increasing utility for scientific research, diagnosing disease, and treatment response prediction. There has been a steady shift away from qualitative assessment toward providing more quantitative scores for these biomarkers. The application of quantitative image analysis has thus become an indispensable tool for in-depth tissue biomarker interrogation in these contexts. This white paper reviews current technologies being employed for quantitative image analysis, their application and pitfalls, regulatory framework demands, and guidelines established for promoting their safe adoption in clinical practice.

Integrating transcriptomics and bulk time course data into a mathematical framework to describe and predict therapeutic resistance in cancer

A significant challenge in the field of biomedicine is the development of methods to integrate the multitude of dispersed data sets into comprehensive frameworks to be used to generate optimal clinical decisions. Recent technological advances in single cell analysis allow for high-dimensional molecular characterization of cells and populations, but to date, few mathematical models have attempted to integrate measurements from the single cell scale with other types of longitudinal data. Here, we present a framework that actionizes static outputs from a machine learning model and leverages these as measurements of state variables in a dynamic model of treatment response. We apply this framework to breast cancer cells to integrate single cell transcriptomic data with longitudinal bulk cell population (bulk time course) data. We demonstrate that the explicit inclusion of the phenotypic composition estimate, derived from single cell RNA-sequencing data (scRNA-seq), improves accuracy in the prediction of new treatments with a concordance correlation coefficient (CCC) of 0.92 compared to a prediction accuracy of CCC = 0.64 when fitting on longitudinal bulk cell population data alone. To our knowledge, this is the first work that explicitly integrates single cell clonally-resolved transcriptome datasets with bulk time-course data to jointly calibrate a mathematical model of drug resistance dynamics. We anticipate this approach to be a first step that demonstrates the feasibility of incorporating multiple data types into mathematical models to develop optimized treatment regimens from data.

Multiparametric Analysis of Longitudinal Quantitative MRI data to Identify Distinct Tumor Habitats in Preclinical Models of Breast Cancer

This study identifies physiological tumor habitats from quantitative magnetic resonance imaging (MRI) data and evaluates their alterations in response to therapy. Two models of breast cancer (BT-474 and MDA-MB-231) were imaged longitudinally with diffusion-weighted MRI and dynamic contrast-enhanced MRI to quantify tumor cellularity and vascularity, respectively, during treatment with trastuzumab or albumin-bound paclitaxel. Tumors were stained for anti-CD31, anti-Ki-67, and H&E. Imaging and histology data were clustered to identify tumor habitats and percent tumor volume (MRI) or area (histology) of each habitat was quantified. Histological habitats were correlated with MRI habitats. Clustering of both the MRI and histology data yielded three clusters: high-vascularity high-cellularity (HV-HC), low-vascularity high-cellularity (LV-HC), and low-vascularity low-cellularity (LV-LC). At day 4, BT-474 tumors treated with trastuzumab showed a decrease in LV-HC (p = 0.03) and increase in HV-HC (p = 0.03) percent tumor volume compared to control. MDA-MB-231 tumors treated with low-dose albumin-bound paclitaxel showed a longitudinal decrease in LV-HC percent tumor volume at day 3 (p = 0.01). Positive correlations were found between histological and imaging-derived habitats: HV-HC (BT-474: p = 0.03), LV-HC (MDA-MB-231: p = 0.04), LV-LC (BT-474: p = 0.04; MDA-MB-231: p < 0.01). Physiologically distinct tumor habitats associated with therapeutic response were identified with MRI and histology data in preclinical models of breast cancer.

AI-based applications in hybrid imaging: how to build smart and truly multi-parametric decision models for radiomics

INTRODUCTION

The quantitative imaging features (radiomics) that can be obtained from the different modalities of current-generation hybrid imaging can give complementary information with regard to the tumour environment, as they measure different morphologic and functional imaging properties. These multi-parametric image descriptors can be combined with artificial intelligence applications into predictive models. It is now the time for hybrid PET/CT and PET/MRI to take the advantage offered by radiomics to assess the added clinical benefit of using multi-parametric models for the personalized diagnosis and prognosis of different disease phenotypes.

OBJECTIVE

The aim of the paper is to provide an overview of current challenges and available solutions to translate radiomics into hybrid PET-CT and PET-MRI imaging for a smart and truly multi-parametric decision model.

Cardiotoxicity and pro-inflammatory effects of the immune checkpoint inhibitor Pembrolizumab associated to Trastuzumab

BACKGROUND

The immunotherapy has revolutionized the world of oncology in the last decades with considerable advantages in terms of overall survival in cancer patients. The association of Pembrolizumab and Trastuzumab was recently proposed in clinical trials for the treatment of Trastuzumab-resistant advanced HER2-positive breast cancer. Although immunotherapies are frequently associated with a wide spectrum of immune-related adverse events, the cardiac toxicity has not been properly studied.

PURPOSE

We studied, for the first time, the putative cardiotoxic and pro-inflammatory effects of Pembrolizumab associated to Trastuzumab.

METHODS

Cell viability, intracellular calcium quantification and pro-inflammatory studies (analyses of the production of Interleukin 1β, 6 and 8, the expression of NF-kB and Leukotriene B4) were performed in human fetal cardiomyocytes. Preclinical studies were also performed in C57BL6 mice by analyzing fibrosis and inflammation in heart tissues.

RESULTS

The combination of Pembrolizumab and Trastuzumab leads to an increase of the intracellular calcium overload (of 3 times compared to untreated cells) and to a reduction of the cardiomyocytes viability (of 65 and 20-25%, compared to untreated and Pembrolizumab or Trastuzumab treated cells, respectively) indicating cardiotoxic effects. Notably, combination therapy increases the inflammation of cardiomyocytes by enhancing the expression of NF-kB and Interleukins. Moreover, in preclinical models, the association of Pembrolizumab and Trastuzumab increases the Interleukins expression of 40-50% compared to the single treatments; the expression of NF-kB and Leukotriene B4 was also increased.

CONCLUSION

Pembrolizumab associated to Trastuzumab leads to strong cardiac pro-inflammatory effects mediated by overexpression of NF-kB and Leukotriene B4 related pathways.